Sonic-gamma ray log analysis improves bit selection

The Donkey Bore Syncline in the Northern Flinders Ranges of South Australia contains a generally finegrained deepwater succession of Early Cambrian age (Bunkers Sandstone) that outcrops on three sides of a syncline and flanks an active salt diapir to the east (Wirrealpa Diapir). Within the succession lies a basal sand-prone interval interpreted as a basin floor fan (BFF) ponded within a mini-basin on a topographically complex slope.The BFF comprises over 30 m of section with deposits that are dominantly massive clean sandstone beds (0.1– 3 m thick) that are stacked or interbedded with siltstones and pinch out along strike.Eight stratigraphic sections and accompanying spectral gamma ray logs (using a hand held scintillometer) were measured through the BFF. Using spectral gamma ray log analysis combined with stratigraphic logs, four alternative correlation panels were constructed.Quantitative analysis of sand-prone intervals interpreted in each of the panels provided data on the vertical and horizontal connectivity within the BFF as different correlation methods were explored and the geological model improved. Quantitative analysis of vertical and horizontal connectivity values indicates a high degree of heterogeneity within the BFF, with poor–moderate vertical connectivity, with individual beds rarely correlating >500 m along strike. This heterogeneity is poorly resolved using conventional wireline log suites, but is greatly improved if spectral gamma ray logs are used (especially Thorium).The data set provides a high-resolution analogue for understanding the internal architecture of deepwater hydrocarbon reservoirs.

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Bayesian deconvolution of gamma‐ray logs

Geophysics ◽

10.1190/1.1442270 ◽

1987 ◽

Vol 52 (11) ◽

pp. 1535-1546 ◽

Cited By ~ 5

Author(s):

Ping Sheng ◽

Benjamin White ◽

Balan Nair ◽

Sandra Kerford

Keyword(s):

High Frequency ◽

Gamma Ray ◽

Simulated Data ◽

Field Measurements ◽

Bayesian Optimization ◽

Frequency Noise ◽

High Frequency Noise ◽

Data Application ◽

Gamma Ray Detector ◽

Gamma Ray Log

The spatial resolution of gamma‐ray logs is defined by the length 𝓁 of the gamma‐ray detector. To resolve thin beds whose thickness is less than 𝓁, it is generally desirable to deconvolve the data to reduce the averaging effect of the detector. However, inherent in the deconvolution operation is an amplification of high‐frequency noise, which can be a detriment to the intended goal of increased resolution. We propose a Bayesian statistical approach to gamma‐ray log deconvolution which is based on optimization of a probability function which takes into account the statistics of gamma‐ray log measurements as well as the empirical information derived from the data. Application of this method to simulated data and to field measurements shows that it is effective in suppressing high‐frequency noise encountered in the deconvolution of gamma‐ray logs. In particular, a comparison with the least‐squares deconvolution approach indicates that the incorporation of physical and statistical information in the Bayesian optimization process results in optimal filtering of the deconvolved results.

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Sequence Stratigraphy of Well DX, Gongola Sub-Basin, Upper Benue Trough, Nigeria

Journal of Applied Sciences and Environmental Management ◽

10.4314/jasem.v23i10.14 ◽

2019 ◽

Vol 23 (10) ◽

pp. 1855-1860

Author(s):

F.O. Amiewalan ◽

E.O. Bamigboye

Keyword(s):

Gamma Ray ◽

Third Order ◽

Depositional Sequence ◽

Benue Trough ◽

Sequence Stratigraphic ◽

Surface System ◽

Sequence Boundaries ◽

System Tracts ◽

Maximum Flooding Surface ◽

Gamma Ray Log

: Biostratigraphic study of Well DX has yielded Cretaceous miospores and dinoflagellates cysts which heightened the recognition of sequence boundaries (SB), Maximum Flooding Surfaces (MFS) and associated Systems Tracts. Five maximum flooding surfaces between 95.6 Ma and 89.0 Ma, four sequence boundaries between 96.4 Ma and 93.0 Ma and threedepositional sequences were identified with varying average thicknesses of sediments interpreted from the gamma ray log and biostratigraphic data. The threedepositional sequences interpreted are -depositional sequence I (96.4 Ma - 95.4 Ma) (8240 ft. - 8120 ft.), depositional sequence II (95.4 Ma - 94.0 Ma) (8120 ft. - 7850 ft.) and depositionalsequence III (94.0 Ma - 93.0 Ma) (7850 ft. - 7550 ft.). All the depositional sequences fall within the third order cycle. The age of the well was attempted based on the presence of some selected marker fossils - Ephedripites spp., Classopollis spp., Spiniferites spp., Cyclonephelium distinctum, Cyclonephelium vannophorum, Subtilisphaera spp., Eucomiidites spp., Triorites africaensis, Odontochitina costata and Droseridites senonicus recovered from the studied intervals and was dated Albian - Santonian. The Sequence stratigraphic interpretations are useful in further deepening the knowledge of thesubsurface geology of the studiedwell in Gongola Sub Basin, Upper Benue Trough of Nigeria.Keywords: Sequence Boundary, Maximum Flooding Surface, System tracts, Depositional sequence

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A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

Journal of Human, Earth, and Future ◽

10.28991/hef-2021-02-02-01 ◽

2021 ◽

Vol 2 (2) ◽

pp. 82-99

Author(s):

Mohsen Talebkeikhah ◽

Zahra Sadeghtabaghi ◽

Mehdi Shabani

Keyword(s):

Neural Network ◽

Random Forest ◽

Gamma Ray ◽

Accurate Determination ◽

Coefficient Of Determination ◽

Support Vector ◽

Learning Approaches ◽

Statistical Parameters ◽

Southwest Of Iran ◽

Gamma Ray Log

Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations. Doi: 10.28991/HEF-2021-02-02-01 Full Text: PDF

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Microfaunal Signals and Paleoenvironmental Reconstruction Sediments in Well Z-1, Offshore Dahomey Basin, South-Western Nigeria

Journal of Geography Environment and Earth Science International ◽

10.9734/jgeesi/2020/v24i330210 ◽

2020 ◽

pp. 50-61

Author(s):

Anthonia Nwanese Asadu ◽

Charles Ojonuba Ameh

Keyword(s):

Gamma Ray ◽

Accessory Mineral ◽

Paleoenvironmental Reconstruction ◽

Planktic Foraminifera ◽

Rock Samples ◽

Foraminiferal Species ◽

Depositional Settings ◽

Dahomey Basin ◽

Abundance And Diversity ◽

Gamma Ray Log

Fifty ditch cutting rock samples from well Z-1, OPL 310 offshore Dahomey basin, south western Nigeria were analyzed for their microfaunal and lithofacies content for the purpose of reconstructing the environment of deposition. Standard techniques of foraminifera slide processing and analysis was followed for the recovery of foraminifera while the gamma ray log complemented the rock samples for the lithofacies analysis. The lithological analysis revealed two lithofacies units in a generally fining upward sequence. The basal sandstone unit is characteristically milky white to brownish, coarse-pebbly grained, sub-angular to round and poorly to well sort with intercalation of shale. This unit is overlain by light to dark grey, moderately hard and non-fissile shale/mudstone sequence with intercalation of sand. Accessory mineral assemblage present in the formations includes mica flakes, glauconite pellets, carbonaceous detritus and ferruginous materials. The basal sandstone unit belong to the Oshosun Formation while the upper shaly unit is typical of Afowo Formation. Microfaunal study showed good recovery of abundant and well diversified planktic and benthic foraminiferal species. Forty-two (42) planktic, sixty-five (65) benthic calcareous and one benthonic arenaceous foraminiferal species were recovered. Micropaleontologically, Paleoenvironmental deductions were based primarily on the assemblage, abundance and diversity of benthic foraminiferal species and presence or absence of planktic foraminifera. Accessory mineral presence also aided the interpretations. Integration of lithological and micropaleontological synthesis enhanced the delineation of two environmental subzones over the analyzed interval, the outer neritic and the upper bathyal depositional settings corresponding to Afowo and Oshosun Formation respectively. A lowstand prograding wedge which is a good exploration target offshore was recognized between intervals 3400 ft to 3500 ft. In conclusion, the rock succession studied, penetrated Afowo and Oshosun Formations, and were deposited in an environment ranging from outer neritic to upper bathyal settings.

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ANALISIS RESERVOAR MIGAS (SANDSTONE) MENGGUNAKAN MULTIATRIBUT SEISMIK PADA LAPANGAN TG12, CEKUNGAN BARITO, KALIMANTAN SELATAN

Jurnal Geofisika Eksplorasi ◽

10.23960/jge.v5i1.19 ◽

2020 ◽

Vol 5 (1) ◽

pp. 3-14

Author(s):

Edo Pratama ◽

Bagus Sapto Mulyatno

Keyword(s):

Seismic Data ◽

Gamma Ray ◽

Regression Method ◽

Well Test ◽

Correlation Result ◽

Neutron Porosity ◽

Gamma Ray Log ◽

Porosity Estimation ◽

Estimation Range ◽

Test Correlation

The study using multi attribute seismic has been done on TG12 field which situated at Lower Foreland Formation, Barito Basin dominated by sandstone on layer area of the target X. The objective of the study is to map the sandstone reservoir by predict distribution value of gamma ray log, neutron porosity, and density which goes through wells such as FM1, FM2, FM3, and FM4 on seismic data. Total attribute that is being used by step wise regression method by considering validation error. Multiattribute process only applied on FM2, FM3, and FM4 wells, whereas FM1 is used as a test well to determine the correlation value between seismic data and log data that is being used. In addition, from well test correlation showing great correlation result of neutron porosity log and density log both obtain the correlation around 0.6322 and 0.6557 while the gamma ray log obtain low correlation that is 0.1647 towards multi attribute result. The processing result of multi attribute obtained distribution of sandstone with gamma ray estimation range value of 65-75.8API, neutron porosity estimation range value 0.15-0.2262, while density estimation range value 2.4308-2.77gr/cc.

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Petrophysical Properties and Digenetic Evolution of Shuaiba Formation in Nasiriyah Oil Field, Southern Iraq

Iraqi Journal of Science ◽

10.24996/ijs.2021.62.12.19 ◽

2021 ◽

pp. 4810-4818

Author(s):

Marwah H. Khudhair

Keyword(s):

Gamma Ray ◽

Water Saturation ◽

Middle Part ◽

Effective Porosity ◽

Oil Field ◽

Neutron Density ◽

Petrophysical Properties ◽

Secondary Porosity ◽

Primary Porosity ◽

Gamma Ray Log

Shuaiba Formation is a carbonate succession deposited within Aptian Sequences. This research deals with the petrophysical and reservoir characterizations characteristics of the interval of interest in five wells of the Nasiriyah oil field. The petrophysical properties were determined by using different types of well logs, such as electric logs (LLS, LLD, MFSL), porosity logs (neutron, density, sonic), as well as gamma ray log. The studied sequence was mostly affected by dolomitization, which changed the lithology of the formation to dolostone and enhanced the secondary porosity that replaced the primary porosity. Depending on gamma ray log response and the shale volume, the formation is classified into three zones. These zones are A, B, and C, each can be split into three rock intervals in respect to the bulk porosity measurements. The resulted porosity intervals are: (I) High to medium effective porosity, (II) High to medium inactive porosity, and (III) Low or non-porosity intervals. In relevance to porosity, resistivity, and water saturation points of view, there are two main reservoir horizon intervals within Shuaiba Formation. Both horizons appear in the middle part of the formation, being located within the wells Ns-1, 2, and 3. These intervals are attributed to high to medium effective porosity, low shale content, and high values of the deep resistivity logs. The second horizon appears clearly in Ns-2 well only.

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